Demographics
By sample
Demographics for included participants, by handedness sample: pilot
righties, new righties, and lefties/mixedies.
| Sample |
N (keepers) |
Age (years) |
Education (years) |
Sex (M/F/O) |
EHI |
| Left/mixedies |
312 |
29 (6.01) |
14.38 (2.3) |
180/126/6 |
-28.65 (66.92) |
| New righties |
205 |
29.58 (6.03) |
14.13 (2.5) |
104/99/2 |
90.06 (18.72) |
| Pilot righties |
104 |
29.48 (5.95) |
14.46 (2.48) |
52/51/1 |
89.54 (14.79) |
By handedness group
Demographics for included participants, by handedness group (EHI
bins).
| Handedness |
N |
Age (years) |
Education (years) |
Sex (M/F/O) |
EHI |
| Left |
171 |
29.14 (6.27) |
14.25 (2.27) |
90/79/2 |
-80.19 (19.96) |
| Mixed |
78 |
29.08 (6.08) |
14.59 (2.32) |
49/27/2 |
-5.61 (26.7) |
| Right |
372 |
29.37 (5.88) |
14.28 (2.47) |
197/170/5 |
88.68 (15.52) |
We have recruited 336 “Right handed”, and 366 “Left handed” or
“Ambidextrous” participants, using Prolific’s pre-screeners.
There are fewer EHI-confirmed left and mixed handers than I expected.
This is, in part, because some of Prolific’s pre-screened “Left-handed”
and “Ambidextrous” participants are fairly right handed, as measured by
the EHI:

If we want to get balanced handedness groups (a more U-shaped EHI
distribution) on Prolific going forward, we should recruit a larger
proportion of “Left-handed” and “Ambidextrous” participants.
Field x Level
Pilot righties (n = 104/112 RH)
In our pilot sample of right handers, do we see the typical field x
level interaction? That is, do participants show a relative bias for
global shapes in the left visual field (LVF)?
Summary. We see the predicted effect, for both
reaction time (28.35ms, 95%CI [13.54, 43.16], p < .001) and accuracy
(OR = 1.6, 95%CI [1.22, 2.28])
Reaction time
Plots


Statistics
Reaction time is modeled as a linear effect of field and level, using
data from every target-present trial with a “go” response:
lmer( rt ~ field + level + field:level + (1 | subject) )
| npar |
AIC |
BIC |
logLik |
deviance |
Chisq |
Df |
p.value |
| 5 |
171,124.179 |
171,161.383 |
−85,557.089 |
171,114.179 |
- |
- |
- |
| 6 |
171,112.1 |
171,156.746 |
−85,550.05 |
171,100.1 |
14.079 |
1 |
.0002 |
| term |
df |
sumsq |
meansq |
statistic |
p.value |
| field |
1 |
16,788.12 |
16,788.12 |
0.226 |
.63 |
| level |
1 |
1,663,708.431 |
1,663,708.431 |
22.38 |
<.0001 |
| field:level |
1 |
512,981.705 |
512,981.705 |
6.9 |
.009 |
| Residuals |
12,590 |
935,944,119.098 |
74,340.28 |
- |
- |
| field_consec |
level_consec |
estimate |
SE |
df |
asymp.LCL |
asymp.UCL |
z.ratio |
p.value |
| LVF - RVF |
Local - Global |
28.351 |
7.555 |
Inf |
13.544 |
43.158 |
3.753 |
.0002 |
| contrast |
estimate |
SE |
df |
asymp.LCL |
asymp.UCL |
z.ratio |
p.value |
| LVF Local - LVF Global |
36.765 |
5.341 |
Inf |
26.296 |
47.233 |
6.883 |
<.0001 |
| RVF Local - RVF Global |
8.413 |
5.347 |
Inf |
−2.067 |
18.893 |
1.573 |
.12 |
| field |
level |
emmean |
SE |
df |
asymp.LCL |
asymp.UCL |
| LVF |
Global |
672.671 |
17.273 |
Inf |
638.817 |
706.525 |
| LVF |
Local |
709.436 |
17.29 |
Inf |
675.548 |
743.323 |
| RVF |
Global |
688.52 |
17.278 |
Inf |
654.656 |
722.385 |
| RVF |
Local |
696.934 |
17.286 |
Inf |
663.054 |
730.813 |
| field |
level |
median |
mean |
SE |
| LVF |
Global |
629 |
671.09 |
4.725 |
| LVF |
Local |
669 |
706.834 |
4.945 |
| RVF |
Global |
649 |
685.85 |
4.883 |
| RVF |
Local |
658 |
696.062 |
4.887 |
Accuracy
Plots


Statistics
Accuracy is modeled as a binomial effect of field and level, using
binary correct/incorrect data from every target-present trial:
glmer( correct ~ field + level + field:level + (1 | subject), family = "binomial" )
| npar |
AIC |
BIC |
logLik |
deviance |
Chisq |
Df |
Pr(>Chisq) |
| 4 |
5,192.89 |
5,222.875 |
−2,592.445 |
5,184.89 |
- |
- |
- |
| 5 |
5,185.098 |
5,222.58 |
−2,587.549 |
5,175.098 |
9.792 |
1 |
0.002 |
| field_consec |
level_consec |
odds.ratio |
SE |
df |
asymp.LCL |
asymp.UCL |
null |
z.ratio |
p.value |
| RVF / LVF |
Local / Global |
1.664 |
0.267 |
Inf |
1.215 |
2.277 |
1 |
3.177 |
.001 |
| contrast |
odds.ratio |
SE |
df |
asymp.LCL |
asymp.UCL |
null |
z.ratio |
p.value |
| LVF Global / LVF Local |
2.287 |
0.268 |
Inf |
1.817 |
2.878 |
1 |
7.054 |
<.0001 |
| RVF Global / RVF Local |
1.375 |
0.15 |
Inf |
1.11 |
1.703 |
1 |
2.914 |
.004 |
| field |
level |
prob |
SE |
df |
asymp.LCL |
asymp.UCL |
| LVF |
Global |
0.977 |
0.003 |
Inf |
0.97 |
0.983 |
| LVF |
Local |
0.949 |
0.006 |
Inf |
0.936 |
0.96 |
| RVF |
Global |
0.968 |
0.004 |
Inf |
0.959 |
0.975 |
| RVF |
Local |
0.956 |
0.005 |
Inf |
0.945 |
0.965 |
| field |
level |
mean_subject_percent_correct |
| LVF |
Global |
96.605 |
| LVF |
Local |
92.819 |
| RVF |
Global |
95.282 |
| RVF |
Local |
93.72 |
New righties (n = 205/224)
Do we replicate the field x level interaction in our new righties?
Summary. We see the predicted
effect, for both reaction time (27.27ms, 95%CI [17.17, 36.37], p <
.001) and accuracy (OR = 2.0, 95%CI [1.6, 2.6])
Reaction time
Plots


Statistics
Reaction time is modeled as a linear effect of field and level, using
data from every target-present trial with a “go” response:
lmer( rt ~ field + level + field:level + (1 | subject) )
| npar |
AIC |
BIC |
logLik |
deviance |
Chisq |
Df |
p.value |
| 5 |
338,385.02 |
338,425.663 |
−169,187.51 |
338,375.02 |
- |
- |
- |
| 6 |
338,359.032 |
338,407.803 |
−169,173.516 |
338,347.032 |
27.988 |
1 |
<.0001 |
| term |
df |
sumsq |
meansq |
statistic |
p.value |
| field |
1 |
60.732 |
60.732 |
0.001 |
.98 |
| level |
1 |
2,006,529.632 |
2,006,529.632 |
28.683 |
<.0001 |
| field:level |
1 |
1,169,823.852 |
1,169,823.852 |
16.722 |
<.0001 |
| Residuals |
25,043 |
1,751,919,309.782 |
69,956.447 |
- |
- |
| field_consec |
level_consec |
estimate |
SE |
df |
asymp.LCL |
asymp.UCL |
z.ratio |
p.value |
| LVF - RVF |
Local - Global |
27.267 |
5.153 |
Inf |
17.167 |
37.366 |
5.292 |
<.0001 |
| contrast |
estimate |
SE |
df |
asymp.LCL |
asymp.UCL |
z.ratio |
p.value |
| LVF Local - LVF Global |
32.585 |
3.638 |
Inf |
25.455 |
39.715 |
8.957 |
<.0001 |
| RVF Local - RVF Global |
5.319 |
3.651 |
Inf |
−1.837 |
12.474 |
1.457 |
.15 |
| field |
level |
emmean |
SE |
df |
asymp.LCL |
asymp.UCL |
| LVF |
Global |
675.866 |
12.113 |
Inf |
652.125 |
699.608 |
| LVF |
Local |
708.452 |
12.123 |
Inf |
684.69 |
732.213 |
| RVF |
Global |
690.156 |
12.119 |
Inf |
666.404 |
713.909 |
| RVF |
Local |
695.475 |
12.122 |
Inf |
671.716 |
719.234 |
| field |
level |
median |
mean |
SE |
| LVF |
Global |
635 |
674.672 |
3.266 |
| LVF |
Local |
663 |
706.194 |
3.403 |
| RVF |
Global |
646 |
687.972 |
3.379 |
| RVF |
Local |
657 |
692.155 |
3.324 |
Accuracy
Plots


Statistics
Accuracy is modeled as a binomial effect of field and level, using
binary correct/incorrect data from every target-present trial:
glmer( correct ~ field + level + field:level + (1 | subject), family = "binomial" )
| npar |
AIC |
BIC |
logLik |
deviance |
Chisq |
Df |
Pr(>Chisq) |
| 4 |
8,994.277 |
9,026.977 |
−4,493.139 |
8,986.277 |
- |
- |
- |
| 5 |
8,964.187 |
9,005.063 |
−4,477.094 |
8,954.187 |
32.09 |
1 |
0 |
| field_consec |
level_consec |
odds.ratio |
SE |
df |
asymp.LCL |
asymp.UCL |
null |
z.ratio |
p.value |
| RVF / LVF |
Local / Global |
2.048 |
0.256 |
Inf |
1.603 |
2.617 |
1 |
5.733 |
<.0001 |
| contrast |
odds.ratio |
SE |
df |
asymp.LCL |
asymp.UCL |
null |
z.ratio |
p.value |
| LVF Global / LVF Local |
2.571 |
0.244 |
Inf |
2.134 |
3.097 |
1 |
9.945 |
<.0001 |
| RVF Global / RVF Local |
1.255 |
0.102 |
Inf |
1.07 |
1.472 |
1 |
2.79 |
.005 |
| field |
level |
prob |
SE |
df |
asymp.LCL |
asymp.UCL |
| LVF |
Global |
0.984 |
0.002 |
Inf |
0.98 |
0.987 |
| LVF |
Local |
0.96 |
0.004 |
Inf |
0.952 |
0.966 |
| RVF |
Global |
0.971 |
0.003 |
Inf |
0.965 |
0.976 |
| RVF |
Local |
0.964 |
0.003 |
Inf |
0.957 |
0.969 |
| field |
level |
mean_subject_percent_correct |
| LVF |
Global |
97.53 |
| LVF |
Local |
94.101 |
| RVF |
Global |
95.595 |
| RVF |
Local |
94.588 |
All righties (n = 309/336)
In our pilot sample of right handers, do we see the typical field x
level interaction? That is, do participants show a relative bias for
global shapes in the left visual field (LVF)?
Summary. We see the predicted effect, for both
reaction time (27.63ms, 95%CI [19.28, 35.98], p < .001) and accuracy
(OR = 1.89, 95%CI [1.56, 2.30])
Reaction time
Plots


Statistics
Reaction time is modeled as a linear effect of field and level, using
data from every target-present trial with a “go” response:
lmer( rt ~ field + level + field:level + (1 | subject) )
| npar |
AIC |
BIC |
logLik |
deviance |
Chisq |
Df |
p.value |
| 5 |
509,525.115 |
509,567.795 |
−254,757.558 |
509,515.115 |
- |
- |
- |
| 6 |
509,485.071 |
509,536.286 |
−254,736.535 |
509,473.071 |
42.045 |
1 |
<.0001 |
| term |
df |
sumsq |
meansq |
statistic |
p.value |
| field |
1 |
4,699.114 |
4,699.114 |
0.066 |
.8 |
| level |
1 |
3,616,074.884 |
3,616,074.884 |
50.633 |
<.0001 |
| field:level |
1 |
1,680,647.538 |
1,680,647.538 |
23.533 |
<.0001 |
| Residuals |
37,637 |
2,687,932,890.743 |
71,417.299 |
- |
- |
| field_consec |
level_consec |
estimate |
SE |
df |
asymp.LCL |
asymp.UCL |
z.ratio |
p.value |
| LVF - RVF |
Local - Global |
27.627 |
4.26 |
Inf |
19.278 |
35.976 |
6.486 |
<.0001 |
| contrast |
estimate |
SE |
df |
asymp.LCL |
asymp.UCL |
z.ratio |
p.value |
| LVF Local - LVF Global |
33.981 |
3.009 |
Inf |
28.084 |
39.878 |
11.294 |
<.0001 |
| RVF Local - RVF Global |
6.354 |
3.017 |
Inf |
0.441 |
12.267 |
2.106 |
.04 |
| field |
level |
emmean |
SE |
df |
asymp.LCL |
asymp.UCL |
| LVF |
Global |
674.796 |
9.903 |
Inf |
655.387 |
694.206 |
| LVF |
Local |
708.777 |
9.912 |
Inf |
689.351 |
728.204 |
| RVF |
Global |
689.606 |
9.907 |
Inf |
670.189 |
709.024 |
| RVF |
Local |
695.96 |
9.91 |
Inf |
676.537 |
715.384 |
| field |
level |
median |
mean |
SE |
| LVF |
Global |
633 |
673.474 |
2.687 |
| LVF |
Local |
665.5 |
706.407 |
2.804 |
| RVF |
Global |
646 |
687.259 |
2.78 |
| RVF |
Local |
658 |
693.462 |
2.75 |
Accuracy
Plots


Statistics
Accuracy is modeled as a binomial effect of field and level, using
binary correct/incorrect data from every target-present trial:
glmer( correct ~ field + level + field:level + (1 | subject), family = "binomial" )
| npar |
AIC |
BIC |
logLik |
deviance |
Chisq |
Df |
Pr(>Chisq) |
| 4 |
14,183.391 |
14,217.732 |
−7,087.695 |
14,175.391 |
- |
- |
- |
| 5 |
14,144.539 |
14,187.466 |
−7,067.269 |
14,134.539 |
40.852 |
1 |
0 |
| field_consec |
level_consec |
odds.ratio |
SE |
df |
asymp.LCL |
asymp.UCL |
null |
z.ratio |
p.value |
| RVF / LVF |
Local / Global |
1.893 |
0.186 |
Inf |
1.56 |
2.296 |
1 |
6.477 |
<.0001 |
| contrast |
odds.ratio |
SE |
df |
asymp.LCL |
asymp.UCL |
null |
z.ratio |
p.value |
| LVF Global / LVF Local |
2.455 |
0.181 |
Inf |
2.124 |
2.837 |
1 |
12.169 |
<.0001 |
| RVF Global / RVF Local |
1.297 |
0.085 |
Inf |
1.141 |
1.474 |
1 |
3.984 |
<.0001 |
| field |
level |
prob |
SE |
df |
asymp.LCL |
asymp.UCL |
| LVF |
Global |
0.982 |
0.002 |
Inf |
0.979 |
0.985 |
| LVF |
Local |
0.957 |
0.003 |
Inf |
0.95 |
0.962 |
| RVF |
Global |
0.97 |
0.002 |
Inf |
0.965 |
0.974 |
| RVF |
Local |
0.961 |
0.003 |
Inf |
0.955 |
0.966 |
| field |
level |
mean_subject_percent_correct |
| LVF |
Global |
97.219 |
| LVF |
Local |
93.669 |
| RVF |
Global |
95.489 |
| RVF |
Local |
94.296 |
Lefties/mixedies (n = 312/336)
In our pilot sample of right handers, do we see the typical field x
level interaction? That is, do participants show a relative bias for
global shapes in the left visual field (LVF)?
Summary. We see an effect in the same
direction as right handers’, for both reaction time (19.85ms, 95%CI
[11.49, 28.22], p < .001) and accuracy (OR = 1.94, 95%CI [1.61,
2.35])
Reaction time
Plots


Statistics
Reaction time is modeled as a linear effect of field and level, using
data from every target-present trial with a “go” response:
lmer( rt ~ field + level + field:level + (1 | subject) )
| npar |
AIC |
BIC |
logLik |
deviance |
Chisq |
Df |
p.value |
| 5 |
512,128.622 |
512,171.324 |
−256,059.311 |
512,118.622 |
- |
- |
- |
| 6 |
512,109.005 |
512,160.248 |
−256,048.503 |
512,097.005 |
21.617 |
1 |
<.0001 |
| term |
df |
sumsq |
meansq |
statistic |
p.value |
| field |
1 |
1,086,635.892 |
1,086,635.892 |
15.996 |
<.0001 |
| level |
1 |
5,787,209 |
5,787,209 |
85.192 |
<.0001 |
| field:level |
1 |
1,123,678.327 |
1,123,678.327 |
16.541 |
<.0001 |
| Residuals |
37,809 |
2,568,431,037.977 |
67,931.737 |
- |
- |
| field_consec |
level_consec |
estimate |
SE |
df |
asymp.LCL |
asymp.UCL |
z.ratio |
p.value |
| LVF - RVF |
Local - Global |
19.854 |
4.27 |
Inf |
11.486 |
28.223 |
4.65 |
<.0001 |
| contrast |
estimate |
SE |
df |
asymp.LCL |
asymp.UCL |
z.ratio |
p.value |
| LVF Local - LVF Global |
34.096 |
3.021 |
Inf |
28.176 |
40.017 |
11.287 |
<.0001 |
| RVF Local - RVF Global |
14.242 |
3.022 |
Inf |
8.319 |
20.165 |
4.713 |
<.0001 |
| field |
level |
emmean |
SE |
df |
asymp.LCL |
asymp.UCL |
| LVF |
Global |
653.142 |
9.222 |
Inf |
635.068 |
671.216 |
| LVF |
Local |
687.238 |
9.235 |
Inf |
669.138 |
705.339 |
| RVF |
Global |
674.032 |
9.225 |
Inf |
655.95 |
692.113 |
| RVF |
Local |
688.274 |
9.232 |
Inf |
670.18 |
706.367 |
| field |
level |
median |
mean |
SE |
| LVF |
Global |
603 |
651.037 |
2.579 |
| LVF |
Local |
647 |
686.683 |
2.744 |
| RVF |
Global |
627 |
672.286 |
2.71 |
| RVF |
Local |
644 |
686.122 |
2.693 |
Accuracy
Plots


Statistics
Accuracy is modeled as a binomial effect of field and level, using
binary correct/incorrect data from every target-present trial:
glmer( correct ~ field + level + field:level + (1 | subject), family = "binomial" )
| npar |
AIC |
BIC |
logLik |
deviance |
Chisq |
Df |
Pr(>Chisq) |
| 4 |
14,943.972 |
14,978.352 |
−7,467.986 |
14,935.972 |
- |
- |
- |
| 5 |
14,899.846 |
14,942.822 |
−7,444.923 |
14,889.846 |
46.125 |
1 |
0 |
| field_consec |
level_consec |
odds.ratio |
SE |
df |
asymp.LCL |
asymp.UCL |
null |
z.ratio |
p.value |
| RVF / LVF |
Local / Global |
1.944 |
0.188 |
Inf |
1.609 |
2.349 |
1 |
6.885 |
<.0001 |
| contrast |
odds.ratio |
SE |
df |
asymp.LCL |
asymp.UCL |
null |
z.ratio |
p.value |
| LVF Global / LVF Local |
3.077 |
0.221 |
Inf |
2.673 |
3.542 |
1 |
15.641 |
<.0001 |
| RVF Global / RVF Local |
1.583 |
0.102 |
Inf |
1.394 |
1.796 |
1 |
7.098 |
<.0001 |
| field |
level |
prob |
SE |
df |
asymp.LCL |
asymp.UCL |
| LVF |
Global |
0.983 |
0.001 |
Inf |
0.98 |
0.986 |
| LVF |
Local |
0.949 |
0.004 |
Inf |
0.941 |
0.956 |
| RVF |
Global |
0.973 |
0.002 |
Inf |
0.968 |
0.977 |
| RVF |
Local |
0.958 |
0.003 |
Inf |
0.951 |
0.963 |
| field |
level |
mean_subject_percent_correct |
| LVF |
Global |
97.196 |
| LVF |
Local |
92.338 |
| RVF |
Global |
95.683 |
| RVF |
Local |
93.52 |